Analysis of cell type fractions estimated from TCGA gene expression and DNA methylation data
Wei Sun
2020-12-31
library(data.table)
library(ggplot2)
library(ggpubr)
library(ggpointdensity)
library(survival)
library(survminer)
library(readxl)
library(stargazer)
library(ggcorrplot)
library(tidyr)
theme_set(theme_bw())1 Loading data
Load the results of clinical information from TCGA pan cancer study.
cancer_types = c("COAD", "LUAD", "LUSC", "SKCM")
cdr = read_excel("clinical_data/TCGA-CDR-SupplementalTableS1.xlsx",
sheet = "TCGA-CDR")
dim(cdr)## [1] 11160 34## # A tibble: 2 x 34
## ...1 bcr_patient_bar… type age_at_initial_… gender race ajcc_pathologic…
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 1 TCGA-OR-A5J1 ACC 58 MALE WHITE Stage II
## 2 2 TCGA-OR-A5J2 ACC 44 FEMALE WHITE Stage IV
## # … with 27 more variables: clinical_stage <chr>, histological_type <chr>,
## # histological_grade <chr>, initial_pathologic_dx_year <dbl>,
## # menopause_status <chr>, birth_days_to <dbl>, vital_status <chr>,
## # tumor_status <chr>, last_contact_days_to <dbl>, death_days_to <dbl>,
## # cause_of_death <chr>, new_tumor_event_type <chr>,
## # new_tumor_event_site <chr>, new_tumor_event_site_other <chr>,
## # new_tumor_event_dx_days_to <dbl>, treatment_outcome_first_course <chr>,
## # margin_status <chr>, residual_tumor <lgl>, OS <dbl>, OS.time <dbl>,
## # DSS <dbl>, DSS.time <dbl>, DFI <dbl>, DFI.time <dbl>, PFI <dbl>,
## # PFI.time <dbl>, Redaction <chr>##
## COAD LUAD LUSC SKCM
## 459 522 504 470Load the results of cell type fraction, and then merage them with clinical information. Save the information for each cancer type into a list named ctf (cell type fractions).
cdr$patient_id = substring(cdr$bcr_patient_barcode, first=9)
ctf = list()
for(c1 in cancer_types){
cat("\n--------------------------------------------------------------\n")
cat(c1)
cat("\n--------------------------------------------------------------\n")
load(sprintf("deconv_expr_%s.RData", c1))
load(sprintf("deconv_methy_%s.RData", c1))
deconv_meth = rho
cat("\ncell type fraction estimates from expression\n")
print(dim(deconv_expr))
print(deconv_expr[1:2,])
cat("\ncell type fraction estimates from methylation\n")
print(dim(deconv_meth))
print(deconv_meth[1,,])
stopifnot(all(rownames(deconv_expr) == dimnames(deconv_meth)[[1]]))
ct = deconv_expr
colnames(ct) = paste0(colnames(ct), ".E")
ct = cbind(ct, deconv_meth[,,"EMeth"])
dim(ct)
ct[1:2,]
ct[which(ct < 5e-3)] = 5e-3
stopifnot(all(rownames(ct) %in% cdr$patient_id))
ct = as.data.frame(ct)
ct$patient_id = rownames(ct)
ct = merge(ct, cdr, by="patient_id")
dim(ct)
ct[1:2,]
ctf[[c1]] = ct
}##
## --------------------------------------------------------------
## COAD
## --------------------------------------------------------------
##
## cell type fraction estimates from expression
## [1] 184 7
## CD4T CD8T Monocyte B NK Neutrophil
## 3697 0.1502354 0.04435527 0.05951967 0.05868135 0.015154031 0.002054261
## 6606 0.1064731 0.01835625 0.03652339 0.02185835 0.008225799 0.000000000
## Treg
## 3697 0.000000000
## 6606 0.008563101
##
## cell type fraction estimates from methylation
## [1] 184 7 5
## EMeth svr ls rls qp
## CD4T 0.04837768 0.06932245 0.00000000 0.00000000 3.300000e-01
## CD8T 0.01617911 0.10795255 0.20038539 0.20908966 -1.299611e-16
## Monocyte 0.05491333 0.05214425 0.04516142 0.03943471 2.873483e-17
## B 0.07897976 0.07224687 0.04444909 0.04062237 -1.387779e-17
## NK 0.02340945 0.01137163 0.00000000 0.00000000 1.115402e-16
## Neutrophil 0.02046280 0.01696225 0.04000410 0.04085326 1.381644e-16
## Treg 0.08767786 0.00000000 0.00000000 0.00000000 0.000000e+00
##
## --------------------------------------------------------------
## LUAD
## --------------------------------------------------------------
##
## cell type fraction estimates from expression
## [1] 414 7
## CD4T CD8T Monocyte B NK Neutrophil
## 6840 0.1070448 0.07211116 0.05125505 0.1181860 0.01410837 0.0009026169
## 6986 0.1750935 0.09420182 0.11945016 0.1515834 0.02003098 0.0184818078
## Treg
## 6840 0.01639197
## 6986 0.02115836
##
## cell type fraction estimates from methylation
## [1] 414 7 5
## EMeth svr ls rls qp
## CD4T 6.527711e-02 0.14571047 0.05004264 0.09353112 3.800000e-01
## CD8T -1.398293e-18 0.00000000 0.08467521 0.05086987 -9.544476e-17
## Monocyte 5.060629e-02 0.08139040 0.08159159 0.07563189 2.529029e-17
## B 5.653567e-02 0.07580389 0.07241928 0.07475072 0.000000e+00
## NK 2.489295e-02 0.03156229 0.03780525 0.04566664 7.711807e-17
## Neutrophil -3.075167e-19 0.01082787 0.02544876 0.02319989 9.586117e-17
## Treg 1.826880e-01 0.03470508 0.02801726 0.01634986 0.000000e+00
##
## --------------------------------------------------------------
## LUSC
## --------------------------------------------------------------
##
## cell type fraction estimates from expression
## [1] 330 7
## CD4T CD8T Monocyte B NK Neutrophil
## 7142 0.2252909 0.09930615 0.1798841 0.05040438 0.02330833 0.02921692
## 7463 0.1925461 0.02496454 0.1619258 0.12780281 0.02999660 0.02857561
## Treg
## 7142 0.002589227
## 7463 0.024188473
##
## cell type fraction estimates from methylation
## [1] 330 7 5
## EMeth svr ls rls qp
## CD4T 3.423551e-01 0.11744624 0.00000000 0.00000000 4.818570e-01
## CD8T -2.948659e-17 0.10997362 0.27732461 0.29192465 0.000000e+00
## Monocyte 6.504418e-02 0.16122402 0.14890235 0.13751388 3.666760e-03
## B 5.448708e-02 0.11639781 0.09307805 0.10095309 8.276993e-02
## NK 1.144374e-18 0.03051041 0.01552118 0.01138641 -8.728890e-18
## Neutrophil 0.000000e+00 0.03049580 0.07517381 0.06822197 7.431181e-17
## Treg 1.481137e-01 0.04395210 0.00000000 0.00000000 4.170631e-02
##
## --------------------------------------------------------------
## SKCM
## --------------------------------------------------------------
##
## cell type fraction estimates from expression
## [1] 393 7
## CD4T CD8T Monocyte B NK Neutrophil
## A8GB 0.13562180 0.1963459 0.06549970 0.1334766 0.06024759 0.005252743
## A8ZN 0.05153736 0.2231810 0.09616168 0.0271029 0.04327248 0.000000000
## Treg
## A8GB 0.07355564
## A8ZN 0.04874455
##
## cell type fraction estimates from methylation
## [1] 393 7 5
## EMeth svr ls rls qp
## CD4T 0.10986798 0.08088028 0.00000000 0.01057198 3.975715e-01
## CD8T 0.16203740 0.14354457 0.22097295 0.23091741 -4.259910e-18
## Monocyte 0.11450338 0.14909764 0.14899821 0.15344431 0.000000e+00
## B 0.19099255 0.17767818 0.14970741 0.17201533 1.674189e-01
## NK 0.03592033 0.04124818 0.04916470 0.02787493 1.356354e-18
## Neutrophil 0.05667836 0.02320019 0.02667351 0.02222075 3.204097e-17
## Treg 0.00000000 0.05435095 0.07448323 0.05295529 1.050096e-012 Asess the association between cell type fractions and survival time
2.1 Association with each cell type
This function cox_summary summarizes the association between cell type composition and survival time.
cox_summary <- function(cell_type1, ct, surv_var){
fm1 = sprintf("Surv(%s.time, %s) ~", surv_var, surv_var)
fm1 = paste(fm1, "age_at_initial_pathologic_diagnosis + gender +")
fm2 = as.formula(paste(fm1, cell_type1))
res.cox = coxph(fm2, data = ct)
x = summary(res.cox)
k = nrow(x$coefficients)
pval = signif(x$coefficients[k,"Pr(>|z|)"], digits=2)
beta = signif(x$coefficients[k, "coef"], digits=3)
HR = signif(x$coefficients[k, "exp(coef)"], digits=3)
HR.confint.lower = sprintf("%.2e", x$conf.int[k, "lower .95"])
HR.confint.upper = sprintf("%.2e", x$conf.int[k, "upper .95"])
HR = paste0(HR, " (", HR.confint.lower, "--", HR.confint.upper, ")")
res = c(beta, HR, pval)
names(res) = c("beta", "HR (95% CI for HR)", "p.value")
return(res)
}Check the associations between cell type fractions and disease-specific survival.
for(c1 in cancer_types){
cat("\n--------------------------------------------------------------\n")
cat(c1)
cat("\n--------------------------------------------------------------\n")
ct = ctf[[c1]]
cell_types = names(ct)[2:15]
for(ctype1 in cell_types){
ct[[ctype1]] = log2(ct[[ctype1]])
}
df1 = t(sapply(cell_types, cox_summary, ct=ct, surv_var="DSS"))
print(as.data.frame(df1))
}##
## --------------------------------------------------------------
## COAD
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E 0.0364 1.04 (5.73e-01--1.88e+00) 0.9
## CD8T.E 0.0347 1.04 (7.26e-01--1.48e+00) 0.85
## Monocyte.E -0.0331 0.967 (6.06e-01--1.54e+00) 0.89
## B.E 0.192 1.21 (8.29e-01--1.77e+00) 0.32
## NK.E 0.209 1.23 (7.99e-01--1.90e+00) 0.34
## Neutrophil.E -0.000735 0.999 (6.78e-01--1.47e+00) 1
## Treg.E -0.0752 0.928 (6.21e-01--1.39e+00) 0.71
## CD4T -0.0329 0.968 (7.03e-01--1.33e+00) 0.84
## CD8T 0.0993 1.1 (8.54e-01--1.43e+00) 0.45
## Monocyte -0.116 0.89 (6.28e-01--1.26e+00) 0.51
## B 0.115 1.12 (7.98e-01--1.58e+00) 0.51
## NK -0.128 0.88 (6.12e-01--1.27e+00) 0.49
## Neutrophil -0.0435 0.957 (7.12e-01--1.29e+00) 0.77
## Treg 0.0856 1.09 (7.94e-01--1.50e+00) 0.6
##
## --------------------------------------------------------------
## LUAD
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E -0.235 0.791 (6.22e-01--1.01e+00) 0.055
## CD8T.E -0.0555 0.946 (7.85e-01--1.14e+00) 0.56
## Monocyte.E -0.0945 0.91 (7.24e-01--1.14e+00) 0.42
## B.E -0.247 0.781 (6.36e-01--9.60e-01) 0.019
## NK.E 0.071 1.07 (8.65e-01--1.33e+00) 0.52
## Neutrophil.E 0.0568 1.06 (9.04e-01--1.24e+00) 0.48
## Treg.E -0.197 0.822 (6.86e-01--9.84e-01) 0.033
## CD4T -0.0584 0.943 (8.49e-01--1.05e+00) 0.28
## CD8T -0.0406 0.96 (8.85e-01--1.04e+00) 0.33
## Monocyte -0.152 0.859 (7.39e-01--9.99e-01) 0.049
## B -0.158 0.854 (7.54e-01--9.67e-01) 0.013
## NK 0.0894 1.09 (9.41e-01--1.27e+00) 0.24
## Neutrophil 0.0377 1.04 (9.19e-01--1.17e+00) 0.55
## Treg 0.0247 1.03 (9.26e-01--1.14e+00) 0.64
##
## --------------------------------------------------------------
## LUSC
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E -0.201 0.818 (6.40e-01--1.05e+00) 0.11
## CD8T.E -0.0989 0.906 (7.09e-01--1.16e+00) 0.43
## Monocyte.E -0.121 0.886 (6.70e-01--1.17e+00) 0.4
## B.E -0.181 0.834 (6.90e-01--1.01e+00) 0.061
## NK.E 0.112 1.12 (8.70e-01--1.44e+00) 0.38
## Neutrophil.E 0.0241 1.02 (8.38e-01--1.25e+00) 0.81
## Treg.E -0.0404 0.96 (7.45e-01--1.24e+00) 0.76
## CD4T -0.00675 0.993 (8.76e-01--1.13e+00) 0.92
## CD8T 0.00542 1.01 (9.07e-01--1.11e+00) 0.92
## Monocyte -0.00151 0.998 (8.48e-01--1.18e+00) 0.99
## B -0.173 0.841 (7.33e-01--9.65e-01) 0.014
## NK -0.073 0.93 (7.89e-01--1.09e+00) 0.38
## Neutrophil -0.0679 0.934 (8.19e-01--1.07e+00) 0.31
## Treg 0.0654 1.07 (9.48e-01--1.20e+00) 0.28
##
## --------------------------------------------------------------
## SKCM
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E -0.147 0.863 (7.74e-01--9.63e-01) 0.0086
## CD8T.E -0.16 0.852 (7.83e-01--9.27e-01) 2e-04
## Monocyte.E -0.0985 0.906 (8.03e-01--1.02e+00) 0.11
## B.E -0.102 0.903 (8.15e-01--1.00e+00) 0.053
## NK.E -0.0537 0.948 (8.12e-01--1.11e+00) 0.5
## Neutrophil.E -0.00292 0.997 (8.34e-01--1.19e+00) 0.97
## Treg.E -0.197 0.821 (7.22e-01--9.33e-01) 0.0026
## CD4T -0.0121 0.988 (9.12e-01--1.07e+00) 0.77
## CD8T -0.135 0.873 (8.17e-01--9.33e-01) 6.3e-05
## Monocyte -0.0394 0.961 (8.86e-01--1.04e+00) 0.35
## B -0.127 0.881 (8.10e-01--9.58e-01) 0.003
## NK -0.0655 0.937 (8.38e-01--1.05e+00) 0.25
## Neutrophil 0.111 1.12 (1.02e+00--1.22e+00) 0.016
## Treg 0.115 1.12 (1.02e+00--1.23e+00) 0.0132.2 Association with relative cell type fractions
Check the associations between disease-specific survival and relative cell type fractions with respect to CD8 T cells.
for(c1 in cancer_types){
cat("\n--------------------------------------------------------------\n")
cat(c1)
cat("\n--------------------------------------------------------------\n")
ct = ctf[[c1]]
cell_types = setdiff(names(ct)[2:15], c("CD8T.E", "CD8T"))
for(ctype1 in cell_types){
ctype1r = paste0(ctype1, "_lr")
ctype2 = "CD8T"
if(grepl(".E", ctype1, fixed=TRUE)){ ctype2 = "CD8T.E"}
# cat(ctype1, ctype2, "\n")
ct[[ctype1r]] = log2(ct[[ctype1]]/ct[[ctype2]])
}
cell_types = paste0(cell_types, "_lr")
df1 = t(sapply(cell_types, cox_summary, ct=ct, surv_var="DSS"))
print(as.data.frame(df1))
}##
## --------------------------------------------------------------
## COAD
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E_lr -0.0295 0.971 (6.42e-01--1.47e+00) 0.89
## Monocyte.E_lr -0.0603 0.942 (6.46e-01--1.37e+00) 0.75
## B.E_lr 0.134 1.14 (8.04e-01--1.63e+00) 0.46
## NK.E_lr 0.0912 1.1 (7.86e-01--1.53e+00) 0.59
## Neutrophil.E_lr -0.0216 0.979 (7.41e-01--1.29e+00) 0.88
## Treg.E_lr -0.0818 0.921 (6.64e-01--1.28e+00) 0.62
## CD4T_lr -0.154 0.857 (6.42e-01--1.14e+00) 0.3
## Monocyte_lr -0.2 0.818 (6.09e-01--1.10e+00) 0.18
## B_lr -0.0669 0.935 (6.43e-01--1.36e+00) 0.73
## NK_lr -0.139 0.871 (6.87e-01--1.10e+00) 0.25
## Neutrophil_lr -0.0714 0.931 (7.72e-01--1.12e+00) 0.45
## Treg_lr -0.0263 0.974 (7.97e-01--1.19e+00) 0.8
##
## --------------------------------------------------------------
## LUAD
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E_lr -0.0784 0.925 (7.48e-01--1.14e+00) 0.47
## Monocyte.E_lr -0.00539 0.995 (8.30e-01--1.19e+00) 0.95
## B.E_lr -0.141 0.869 (7.14e-01--1.06e+00) 0.16
## NK.E_lr 0.0752 1.08 (9.23e-01--1.26e+00) 0.34
## Neutrophil.E_lr 0.0574 1.06 (9.38e-01--1.20e+00) 0.35
## Treg.E_lr -0.104 0.901 (7.77e-01--1.05e+00) 0.17
## CD4T_lr 0.00175 1 (9.50e-01--1.06e+00) 0.95
## Monocyte_lr 0.00187 1 (9.16e-01--1.10e+00) 0.97
## B_lr -0.0338 0.967 (8.67e-01--1.08e+00) 0.54
## NK_lr 0.0582 1.06 (9.82e-01--1.14e+00) 0.13
## Neutrophil_lr 0.0415 1.04 (9.72e-01--1.12e+00) 0.24
## Treg_lr 0.0207 1.02 (9.71e-01--1.07e+00) 0.41
##
## --------------------------------------------------------------
## LUSC
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E_lr -0.11 0.896 (6.65e-01--1.21e+00) 0.47
## Monocyte.E_lr 0.00935 1.01 (7.84e-01--1.30e+00) 0.94
## B.E_lr -0.113 0.893 (7.38e-01--1.08e+00) 0.24
## NK.E_lr 0.127 1.14 (9.33e-01--1.38e+00) 0.2
## Neutrophil.E_lr 0.0549 1.06 (9.04e-01--1.23e+00) 0.49
## Treg.E_lr 0.0396 1.04 (8.50e-01--1.27e+00) 0.7
## CD4T_lr -0.005 0.995 (9.25e-01--1.07e+00) 0.89
## Monocyte_lr -0.0066 0.993 (8.92e-01--1.11e+00) 0.9
## B_lr -0.172 0.842 (7.28e-01--9.75e-01) 0.021
## NK_lr -0.0264 0.974 (8.90e-01--1.07e+00) 0.57
## Neutrophil_lr -0.0271 0.973 (9.01e-01--1.05e+00) 0.49
## Treg_lr 0.0165 1.02 (9.54e-01--1.08e+00) 0.61
##
## --------------------------------------------------------------
## SKCM
## --------------------------------------------------------------
## beta HR (95% CI for HR) p.value
## CD4T.E_lr 0.192 1.21 (1.06e+00--1.39e+00) 0.0056
## Monocyte.E_lr 0.213 1.24 (1.10e+00--1.39e+00) 0.00031
## B.E_lr 0.121 1.13 (1.02e+00--1.25e+00) 0.021
## NK.E_lr 0.182 1.2 (1.09e+00--1.32e+00) 0.00026
## Neutrophil.E_lr 0.145 1.16 (1.07e+00--1.25e+00) 0.00038
## Treg.E_lr 0.157 1.17 (1.03e+00--1.33e+00) 0.02
## CD4T_lr 0.0918 1.1 (1.04e+00--1.16e+00) 0.0012
## Monocyte_lr 0.121 1.13 (1.05e+00--1.21e+00) 0.00049
## B_lr 0.0716 1.07 (9.98e-01--1.16e+00) 0.055
## NK_lr 0.084 1.09 (1.03e+00--1.15e+00) 0.0037
## Neutrophil_lr 0.0974 1.1 (1.05e+00--1.16e+00) 5.4e-05
## Treg_lr 0.0927 1.1 (1.05e+00--1.15e+00) 5.9e-05Generate plots of survival curves with respect to cell type fractions for SKCM.
ct = ctf[["SKCM"]]
cell_types = c("CD4T", "CD8T", "Monocyte", "B", "NK", "Neutrophil", "Treg")
glist = list()
for(ctype1 in cell_types){
ctype1E = paste0(ctype1, ".E")
ab = rep("Low", nrow(ct))
ab[which(ct[[ctype1E]] > median(ct[[ctype1E]]))] = "High"
fit1 = survfit(Surv(DSS.time, DSS) ~ ab, data=ct)
g1 = ggsurvplot(fit1, data = ct, risk.table = TRUE,
pval=TRUE, pval.method=TRUE,
title=paste(ctype1, ".E"))
ab = rep("Low", nrow(ct))
ab[which(ct[[ctype1]] > median(ct[[ctype1]]))] = "High"
fit2 = survfit(Surv(DSS.time, DSS) ~ ab, data=ct)
g2 = ggsurvplot(fit2, data = ct, risk.table = TRUE,
pval=TRUE, pval.method=TRUE,
title=ctype1)
print(g1)
print(g2)
}
Generate plots of survival curves with respect to relative cell type fractions for SKCM.
ct = ctf[["SKCM"]]
cell_types = c("CD4T", "Monocyte", "B", "NK", "Neutrophil", "Treg")
glist = list()
for(ctype1 in cell_types){
ctype1E = paste0(ctype1, ".E")
ct.lrE = log2(ct[[ctype1E]]/ct[["CD8T.E"]])
rel.ab = rep("Low", length(ct.lrE))
rel.ab[which(ct.lrE > median(ct.lrE))] = "High"
fit1 = survfit(Surv(DSS.time, DSS) ~ rel.ab, data=ct)
g1 = ggsurvplot(fit1, data = ct, risk.table = TRUE,
pval=TRUE, pval.method=TRUE,
title=paste(ctype1, ".E"))
ct.lr = log2(ct[[ctype1]]/ct[["CD8T"]])
rel.ab = rep("Low", length(ct.lr))
rel.ab[which(ct.lr > median(ct.lr))] = "High"
fit2 = survfit(Surv(DSS.time, DSS) ~ rel.ab, data=ct)
g2 = ggsurvplot(fit2, data = ct, risk.table = TRUE,
pval=TRUE, pval.method=TRUE,
title=ctype1)
print(g1)
print(g2)
}
3 Assess the associations between mutation load and cell type fractions
First load mutation burden data
scL = list()
for(c1 in cancer_types){
sc = readRDS(sprintf("clinical_data/%s_somatic_clinic.rds", c1))
dim(sc)
names(sc)
sc = sc[,-(23:32)]
dim(sc)
sc[1:2,]
names(sc)[1] = "bcr_patient_barcode"
ct = ctf[[c1]]
dim(ct)
ct[1:2,]
sc = merge(sc, ct, by="bcr_patient_barcode")
dim(sc)
sc[1:2,]
scL[[c1]] = sc
}
lapply(ctf, dim)## $COAD
## [1] 184 49
##
## $LUAD
## [1] 414 49
##
## $LUSC
## [1] 330 49
##
## $SKCM
## [1] 393 49## $COAD
## [1] 176 76
##
## $LUAD
## [1] 376 76
##
## $LUSC
## [1] 307 76
##
## $SKCM
## [1] 359 76Next assess the associations between mutation load and cell type fractions
for(c1 in cancer_types){
cat("\n--------------------------------------------------------------\n")
cat(c1)
cat("\n--------------------------------------------------------------\n")
sc = scL[[c1]]
mb1 = sc$raw_MB_hg38_SNV + sc$raw_MB_hg38_INDEL
mb2 = sc$raw_MB_hg38_SNV*sc$prop_clonal + sc$raw_MB_hg38_INDEL
mb = cbind(mb1, mb2)
mb[mb < 10] = NA
mb = log10(mb)
cf = sc[,30:43]
cr1 = cor(mb, cf, method="spearman", use="pairwise.complete.obs")
pv1 = matrix(NA, nrow=2, ncol=14)
for(j in 1:2){
for(k in 1:14){
cjk = cor.test(mb[,j], cf[,k], method="spearman")
pv1[j,k] = cjk$p.value
}
}
cp = cbind(round(t(cr1),3), signif(t(pv1),2))
colnames(cp)[3:4] = c("mb1.pval", "mb2.pval")
print(cp)
crE = cr1[,1:7]
colnames(crE) = gsub(".E", "", colnames(crE))
gc1 = ggcorrplot(crE, lab = TRUE, title=paste0(c1, "-Expr"))
gc2 = ggcorrplot(cr1[,8:14], lab = TRUE, title=paste0(c1, "-Meth"))
gga = ggarrange(gc1, gc2 + rremove("legend"), ncol = 2, nrow = 1)
print(gga)
}##
## --------------------------------------------------------------
## COAD
## --------------------------------------------------------------
## mb1 mb2 mb1.pval mb2.pval
## CD4T.E -0.020 -0.057 0.7900 0.4600
## CD8T.E 0.213 0.192 0.0046 0.0110
## Monocyte.E -0.069 -0.138 0.3600 0.0680
## B.E -0.134 -0.189 0.0770 0.0120
## NK.E 0.215 0.199 0.0041 0.0081
## Neutrophil.E 0.089 0.104 0.2400 0.1700
## Treg.E -0.031 -0.119 0.6800 0.1100
## CD4T -0.139 -0.174 0.0660 0.0210
## CD8T 0.226 0.203 0.0026 0.0068
## Monocyte 0.006 -0.071 0.9400 0.3500
## B 0.009 -0.057 0.9100 0.4500
## NK 0.013 -0.012 0.8600 0.8700
## Neutrophil -0.001 -0.010 0.9900 0.8900
## Treg -0.154 -0.153 0.0410 0.0430
##
## --------------------------------------------------------------
## LUAD
## --------------------------------------------------------------
## mb1 mb2 mb1.pval mb2.pval
## CD4T.E -0.067 -0.055 2.0e-01 2.9e-01
## CD8T.E 0.230 0.200 6.8e-06 1.3e-04
## Monocyte.E -0.124 -0.100 1.7e-02 5.6e-02
## B.E 0.018 0.000 7.3e-01 9.9e-01
## NK.E -0.015 -0.009 7.7e-01 8.7e-01
## Neutrophil.E -0.074 -0.078 1.5e-01 1.4e-01
## Treg.E -0.156 -0.131 2.5e-03 1.2e-02
## CD4T -0.334 -0.266 3.4e-11 2.8e-07
## CD8T 0.162 0.136 1.6e-03 9.5e-03
## Monocyte 0.003 0.025 9.6e-01 6.4e-01
## B 0.026 0.008 6.1e-01 8.9e-01
## NK 0.067 0.054 1.9e-01 3.1e-01
## Neutrophil -0.098 -0.118 5.9e-02 2.5e-02
## Treg -0.155 -0.152 2.6e-03 3.8e-03
##
## --------------------------------------------------------------
## LUSC
## --------------------------------------------------------------
## mb1 mb2 mb1.pval mb2.pval
## CD4T.E -0.175 -0.216 2.1e-03 1.5e-04
## CD8T.E -0.127 -0.204 2.6e-02 3.4e-04
## Monocyte.E -0.254 -0.234 6.6e-06 3.7e-05
## B.E -0.125 -0.167 2.9e-02 3.6e-03
## NK.E 0.040 0.024 4.8e-01 6.7e-01
## Neutrophil.E -0.071 -0.053 2.2e-01 3.6e-01
## Treg.E -0.149 -0.177 9.2e-03 2.0e-03
## CD4T 0.115 0.142 4.5e-02 1.3e-02
## CD8T -0.171 -0.267 2.7e-03 2.3e-06
## Monocyte -0.142 -0.119 1.3e-02 3.9e-02
## B -0.188 -0.250 9.3e-04 1.1e-05
## NK -0.115 -0.094 4.5e-02 1.0e-01
## Neutrophil -0.071 -0.052 2.2e-01 3.7e-01
## Treg 0.170 0.202 2.9e-03 4.0e-04
##
## --------------------------------------------------------------
## SKCM
## --------------------------------------------------------------
## mb1 mb2 mb1.pval mb2.pval
## CD4T.E 0.038 0.013 0.4800 0.820
## CD8T.E 0.110 0.059 0.0390 0.270
## Monocyte.E -0.004 -0.020 0.9500 0.710
## B.E 0.032 0.007 0.5500 0.900
## NK.E -0.033 -0.005 0.5300 0.920
## Neutrophil.E -0.053 -0.001 0.3200 0.980
## Treg.E 0.031 -0.032 0.5600 0.560
## CD4T -0.074 -0.069 0.1600 0.200
## CD8T 0.146 0.093 0.0059 0.085
## Monocyte 0.021 0.033 0.6900 0.540
## B 0.044 0.014 0.4100 0.790
## NK 0.089 0.116 0.0930 0.031
## Neutrophil -0.056 -0.004 0.2900 0.940
## Treg -0.091 -0.097 0.0860 0.072
Illustrate cell type fraction estimates
## $COAD
## [1] 176 76
##
## $LUAD
## [1] 376 76
##
## $LUSC
## [1] 307 76
##
## $SKCM
## [1] 359 76## [1] 1218 76## bcr_patient_barcode SMASH_S_hg38 SMASH_oE_hg38 SMASH_wE_hg38 SMASH_oNE_hg38
## 1 TCGA-A6-2671 3 1.06 0.9274599 0.5531430
## 2 TCGA-A6-2675 2 0.68 0.7213201 0.6600493
## SMASH_wNE_hg38 AscatPurity AscatPloidy raw_MB_hg38_SNV raw_MB_hg38_INDEL
## 1 0.4846527 0.68 3.609337 49 7
## 2 0.6585205 0.49 3.351309 72 2
## IDH_CNV_status_hg38 tCN_burden tCN_burden_ap tumor_type.x num_clonal
## 1 IDH wild type 2.228662 1.0712969 COAD 39
## 2 IDH wild type 1.572784 0.8041944 COAD 46
## num_subclonal prop_clonal gender.x age tumor_type.y
## 1 10 0.7959184 MALE 85 Colon
## 2 26 0.6388889 MALE 78 Colon
## initial_pathologic_diagnosis_method histological_type.x
## 1 <NA> Colon Adenocarcinoma
## 2 <NA> Colon Adenocarcinoma
## yr_of_tobacco_smoking_onset pathologic_T stage Time Delta
## 1 <NA> T3 IV 1331.01 1
## 2 <NA> T3 II 1321.01 0
## radiation_therapy patient_id CD4T.E CD8T.E Monocyte.E B.E
## 1 NO 2671 0.07665117 0.03053652 0.07737681 0.01560974
## 2 NO 2675 0.12102204 0.04654293 0.16303034 0.13314210
## NK.E Neutrophil.E Treg.E CD4T CD8T Monocyte
## 1 0.01773015 0.03318939 0.01890621 0.04401678 0.00500000 0.06109717
## 2 0.02475289 0.03814128 0.05336841 0.17399563 0.09486904 0.07275387
## B NK Neutrophil Treg ...1 type
## 1 0.02888513 0.00500000 0.05312428 0.08287664 1961 COAD
## 2 0.06724810 0.03319116 0.03366724 0.10427496 1964 COAD
## age_at_initial_pathologic_diagnosis gender.y race
## 1 85 MALE WHITE
## 2 78 MALE WHITE
## ajcc_pathologic_tumor_stage clinical_stage histological_type.y
## 1 Stage IV [Not Applicable] Colon Adenocarcinoma
## 2 Stage IIA [Not Applicable] Colon Adenocarcinoma
## histological_grade initial_pathologic_dx_year menopause_status birth_days_to
## 1 [Not Available] 2009 [Not Available] -31329
## 2 [Not Available] 2009 [Not Available] -28813
## vital_status tumor_status last_contact_days_to death_days_to cause_of_death
## 1 Dead WITH TUMOR NA 1331 [Not Available]
## 2 Alive TUMOR FREE 1321 NA [Not Available]
## new_tumor_event_type new_tumor_event_site new_tumor_event_site_other
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## new_tumor_event_dx_days_to treatment_outcome_first_course margin_status
## 1 535 [Not Available] <NA>
## 2 NA Complete Remission/Response <NA>
## residual_tumor OS OS.time DSS DSS.time DFI DFI.time PFI PFI.time Redaction
## 1 NA 1 1331 1 1331 NA NA 1 535 <NA>
## 2 NA 0 1321 0 1321 0 1321 0 1321 <NA>## [1] "bcr_patient_barcode" "SMASH_S_hg38"
## [3] "SMASH_oE_hg38" "SMASH_wE_hg38"
## [5] "SMASH_oNE_hg38" "SMASH_wNE_hg38"
## [7] "AscatPurity" "AscatPloidy"
## [9] "raw_MB_hg38_SNV" "raw_MB_hg38_INDEL"
## [11] "IDH_CNV_status_hg38" "tCN_burden"
## [13] "tCN_burden_ap" "tumor_type.x"
## [15] "num_clonal" "num_subclonal"
## [17] "prop_clonal" "gender.x"
## [19] "age" "tumor_type.y"
## [21] "initial_pathologic_diagnosis_method" "histological_type.x"
## [23] "yr_of_tobacco_smoking_onset" "pathologic_T"
## [25] "stage" "Time"
## [27] "Delta" "radiation_therapy"
## [29] "patient_id" "CD4T.E"
## [31] "CD8T.E" "Monocyte.E"
## [33] "B.E" "NK.E"
## [35] "Neutrophil.E" "Treg.E"
## [37] "CD4T" "CD8T"
## [39] "Monocyte" "B"
## [41] "NK" "Neutrophil"
## [43] "Treg" "...1"
## [45] "type" "age_at_initial_pathologic_diagnosis"
## [47] "gender.y" "race"
## [49] "ajcc_pathologic_tumor_stage" "clinical_stage"
## [51] "histological_type.y" "histological_grade"
## [53] "initial_pathologic_dx_year" "menopause_status"
## [55] "birth_days_to" "vital_status"
## [57] "tumor_status" "last_contact_days_to"
## [59] "death_days_to" "cause_of_death"
## [61] "new_tumor_event_type" "new_tumor_event_site"
## [63] "new_tumor_event_site_other" "new_tumor_event_dx_days_to"
## [65] "treatment_outcome_first_course" "margin_status"
## [67] "residual_tumor" "OS"
## [69] "OS.time" "DSS"
## [71] "DSS.time" "DFI"
## [73] "DFI.time" "PFI"
## [75] "PFI.time" "Redaction"names(scDf)[14] = "cancer_type"
scE = scDf[,c(14,30:36)]
rhos = scE %>% pivot_longer(-cancer_type, names_to=c("cell_type"))
ggplot(rhos, aes(x=cell_type, y=value, fill=cancer_type)) +
geom_boxplot() + labs(title="Cell type fraction by gene expression")
scM = scDf[,c(14,37:43)]
rhos = scM %>% pivot_longer(-cancer_type, names_to=c("cell_type"))
ggplot(rhos, aes(x=cell_type, y=value, fill=cancer_type)) +
geom_boxplot() + labs(title="Cell type fraction by DNA methylation")
Compare cell type fraction estimates between gene expression and DNA methylation
cell_types = c("B", "CD4T", "CD8T", "Monocyte", "NK", "Neutrophil", "Treg")
for(cc1 in cell_types){
g1 = ggplot(scDf, aes_string(x=paste0(cc1, ".E"), y=cc1, color="cancer_type")) +
geom_point() + geom_smooth(method=lm) + facet_grid(~ cancer_type) +
theme(legend.position = "none")
print(g1)
}
Compare cell type fraction estimates vs. mutation load
cell_types = c("B", "CD4T", "CD8T", "Monocyte", "NK", "Neutrophil", "Treg")
mb = scDf$raw_MB_hg38_SNV + scDf$raw_MB_hg38_INDEL
mb[which(mb < 10)] = NA
scDf$mb = log10(mb)
for(cc1 in cell_types){
g1 = ggplot(scDf, aes_string(x="mb", y=paste0(cc1, ".E"), color="cancer_type")) +
geom_point() + geom_smooth(method=lm) + facet_grid(~ cancer_type) +
theme(legend.position = "none")
print(g1)
g1 = ggplot(scDf, aes_string(x="mb", y=cc1, color="cancer_type")) +
geom_point() + geom_smooth(method=lm) + facet_grid(~ cancer_type) +
theme(legend.position = "none")
print(g1)
}
4 Association between hypermutation status in COAD and immune cell fractions.
First define the hypermutation subgroup with larger number of mutations.
coad = scL[["COAD"]]
ggplot(coad, aes(x=log10(raw_MB_hg38_SNV)))+
geom_histogram(color="darkblue", fill="lightblue") +
geom_vline(xintercept = log10(400))
Next compare the cell type fractions between hypermuted and non-hypermutated groups.
ctypes = c("CD8T.E", "CD8T", "Treg.E", "Treg")
gl = list()
for(ct1 in ctypes){
w1 = wilcox.test(coad[[ct1]]~ coad$hyper_mut)
gl[[ct1]] = ggplot(coad, aes_string(x="hyper_mut", y=ct1, fill="hyper_mut")) +
theme(legend.position = "none") + geom_violin(trim=FALSE) +
geom_boxplot(width=0.1, fill="white") +
labs(title=sprintf("Wilcox p=%.1e", w1$p.value), x="Hypermutation status")
}
ggarrange(gl[[1]], gl[[2]], gl[[3]], gl[[4]], nrow = 2, ncol = 2)
5 Session information
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 2278615 121.7 4316380 230.6 NA 4316380 230.6
## Vcells 4380151 33.5 10146329 77.5 32768 8885257 67.8## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyr_1.0.2 ggcorrplot_0.1.3 stargazer_5.2.2
## [4] readxl_1.3.1 survminer_0.4.8 survival_3.1-8
## [7] ggpointdensity_0.1.0 ggpubr_0.4.0.999 ggplot2_3.3.1
## [10] data.table_1.12.8
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 lattice_0.20-38 zoo_1.8-7 utf8_1.1.4
## [5] assertthat_0.2.1 digest_0.6.23 plyr_1.8.5 R6_2.4.1
## [9] cellranger_1.1.0 backports_1.1.5 evaluate_0.14 pillar_1.4.3
## [13] rlang_0.4.6 curl_4.3 car_3.0-8 Matrix_1.2-18
## [17] rmarkdown_2.1 labeling_0.3 splines_3.6.2 stringr_1.4.0
## [21] foreign_0.8-75 munsell_0.5.0 broom_0.5.6 compiler_3.6.2
## [25] xfun_0.12 pkgconfig_2.0.3 mgcv_1.8-31 htmltools_0.4.0
## [29] tidyselect_1.0.0 tibble_3.0.1 gridExtra_2.3 km.ci_0.5-2
## [33] rio_0.5.16 fansi_0.4.1 crayon_1.3.4 dplyr_0.8.4
## [37] withr_2.1.2 grid_3.6.2 nlme_3.1-144 xtable_1.8-4
## [41] gtable_0.3.0 lifecycle_0.2.0 magrittr_1.5 KMsurv_0.1-5
## [45] scales_1.1.0 zip_2.0.4 cli_2.0.1 stringi_1.4.5
## [49] carData_3.0-4 reshape2_1.4.3 farver_2.0.3 ggsignif_0.6.0
## [53] ellipsis_0.3.0 survMisc_0.5.5 generics_0.0.2 vctrs_0.3.0
## [57] cowplot_1.0.0 openxlsx_4.1.5 tools_3.6.2 forcats_0.5.0
## [61] glue_1.3.1 purrr_0.3.3 hms_0.5.3 abind_1.4-5
## [65] yaml_2.2.1 colorspace_1.4-1 rstatix_0.6.0 knitr_1.28
## [69] haven_2.3.0